Autonomous vehicles have the ability to operate without the intervention of a human operator, e.g., driver, that is, a vehicle controller makes decisions about accelerating, braking, and/or steering the vehicle. A vehicle may be fully autonomous or semi-autonomous. A semi-autonomous vehicle may be autonomous only in particular situations, for example, highway driving or parallel parking, or with respect to certain vehicle subsystems, for example, braking but not acceleration or steering.
An autonomous vehicle may include sensors for tracking an external environment surrounding the vehicle. Some types of sensors are radar sensors, scanning laser range finders, light detection and ranging (LIDAR) devices, and image processing sensors such as cameras. The vehicle controller is in communication with the sensors and uses output from the sensors to analyze the external environment, for example, defining features of a surrounding landscape, detecting roads and lanes of roads on the landscape, interpreting signs and signals, and tracking and classifying objects in the environment such as vehicles, cyclists, and pedestrians. For example, a vehicle controller may classify whether a detected object is another vehicle and provide state information about the other vehicle, such as location, speed, and heading.
The vehicle controller uses target path prediction to predict where another vehicle will travel. The vehicle controller uses the predicted path of the other vehicle to make decisions affecting operation of the vehicle. Thus, inaccuracies and failures of current path prediction technologies are problematic. There is opportunity to improve technology for predicting paths of objects such as target vehicles.